By Topic

Adaptive parallelism and Piranha

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
N. Carriero ; Dept. of Comput. Sci., Yale Univ., New Haven, CT, USA ; E. Freeman ; D. Gelernter ; D. Kaminsky

Desktop computers are idle much of the time. Ongoing trends make aggregate LAN “waste”-idle compute cycles-an increasingly attractive target for recycling. Piranha, a software implementation of adaptive parallelism, allows these waste cycles to be recaptured by putting them to work running parallel applications. Most parallel processing is static: programs execute on a fixed set of processors throughout a computation. Adaptive parallelism allows for dynamic processor sets which means that the number of processors working on a computation may vary, depending on availability. With adaptive parallelism, instead of parceling out jobs to idle workstations, a single job is distributed over many workstations. Adaptive parallelism is potentially valuable on dedicated multiprocessors as well, particularly on massively parallel processors. One key Piranha advantage is that task descriptors, not processes, are the basic movable, remappable computation unit. The task descriptor approach supports strong heterogeneity. A process image representing a task in mid computation can't be moved to a machine of a different type, but a task descriptor can be. Thus, a task begun on a Sun computer can be completed by an IBM machine. The authors show that adaptive parallelism has the potential to integrate heterogeneous platforms seamlessly into a unified computing resource and to permit more efficient sharing of traditional parallel processors than is possible with current systems

Published in:

Computer  (Volume:28 ,  Issue: 1 )